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We study the application of active learning techniques to the translation of unbounded data streams via interactive neural machine translation. The main idea is to select, from an unbounded stream of source sentences, those worth to be supervised by a human agent. The user will interactively translate those samples. Once validated, these data is useful for adapting the neural machine translation model.We propose two novel methods for selecting the samples to be validated. We exploit the information from the attention mechanism of a neural machine translation system. Our experiments show that the inclusion of active learning techniques into this pipeline allows to reduce the effort required during the process, while increasing the quality of the translation system. Moreover, it enables to balance the human effort required for achieving a certain translation quality. Moreover, our neural system outperforms classical approaches by a large margin.

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Neural machine translation systems require large amounts of training data and resources. Even with this, the quality of the translations may be insufficient for some users or domains. In such cases, the output of the system must be revised by a human agent. This can be done in a post-editing stage or following an interactive machine translation protocol.We explore the incremental update of neural machine translation systems during the post-editing or interactive translation processes. Such modifications aim to incorporate the new knowledge, from the edited sentences, into the translation system. Updates to the model are performed on-the-fly, as sentences are corrected, via online learning techniques. In addition, we implement a novel interactive, adaptive system, able to react to single-character interactions. This system greatly reduces the human effort required for obtaining high-quality translations.In order to stress our proposals, we conduct exhaustive experiments varying the amount and type of data available for training. Results show that online learning effectively achieves the objective of reducing the human effort required during the post-editing or the interactive machine translation stages. Moreover, these adaptive systems also perform well in scenarios with scarce resources. We show that a neural machine translation system can be rapidly adapted to a specific domain, exclusively by means of online learning techniques.3. We conduct a large experimentation, using public corpora containing different features from varied domains. We stress the translation systems, applying them in three different translation scenarios. 4. We show that OL brings significant improvements to the translation engines, in terms of translation quality and human effort reduction. Comparisons with other works in the literature also show that our adaptive, interactive systems are able to outperform the existing state-of-the-art. 5. We open-source all code developed in this work, in order to make research reproducible.The rest of this manuscript is structured as follows: the related work is reviewed in Section 2. Next, Section 3 briefly introduces the NMT technology, while the interactive protocol for NMT is presented in Section 4. OL is described and applied together with INMT in Section 5. Section 6 describes the experimental setup of this work. Results are presented and discussed in Section 7. Finally, we conclude the work and trace future lines of work in Section 8. Related workThis work puts together three thoroughly studied fields: neural machine translation, interactive machine translation and online learning. In this section, we briefly review the progress made in the last years in each one of these fields. Neural machine translationAlthough the first attempts of performing machine translation with neural networks date from long ago (Castaño and Casacuberta, 1997; Forcada andÑeco, 1997), NMT only took off recently. Kalchbrenner and Blunsom (2013) reintroduced full neural MT, although the results were non-competitive with re...